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There are large collections and sets of
data about the patients, diagnosis, treatment
procedures etc... in the field of medicine today. Using
data mining techniques in those cases provide a
statistical and logical analysis of the data looking for
patterns that can aid by decision making and
prediction.
In this paper we presents study of using Bayesian
network (BN) in the domain of emergency medicine
where BN are especially appropriate because of their
symbolic representation, handling of uncertainity,
where different scenarios are possible by given
evidences. We show a use of BN in the case of study
out-of-hospital cardiac arrest in emergency medicine,
where we use the BN as tool for prediction of return
of spontaneous circulation (ROSC) and survival of
hospital discharge.
Based on experiences we conclude the paper with
discussion of some future applications of using
Bayesian networks on human patient simulators in
emergency medicine.

Keywords: Data mining, machine learning,
Bayesian networks, emergency medicine, human
patient simulators